Fingerprint recognition technology, as a type of biometric technology, has been widely used in our daily life, from mobile phone unlocking, access control systems to payment verification, etc., it plays an important role. So, how exactly does fingerprint recognition technology work? This article will demystify fingerprint recognition for you and take you to understand its principle and application.
Fingerprint recognition technology is mainly based on the uniqueness and stability of fingerprints. Each person's fingerprint is unique, and even identical twins have differences in their fingerprints. At the same time, fingerprints remain largely unchanged throughout a person's life, so they have a high degree of stability. Fingerprint recognition technology realizes the identification of individual identity through the collection, processing and comparison of fingerprint images.
Specifically, fingerprint recognition technology includes the following steps:
Fingerprinting: Through special fingerprint acquisition equipment, such as optical fingerprint sensor, capacitive fingerprint sensor, etc., the fingerprint image is converted into a digital signal for subsequent processing.
Pretreatment: Denoise, enhance, and refine the collected fingerprint images to improve the quality and recognition rate of the fingerprint images.
Feature extraction: Extract representative feature points from the preprocessed fingerprint image, such as the endpoints of the fingerprint's lines and bifurcations, and these feature points constitute the feature template of the fingerprint.
Comparison and identification: The extracted feature template is compared with the template in the database, and the template with the highest similarity is found through the matching algorithm, so as to realize the identification of individual identity.
Fingerprint collection is the first step of fingerprint recognition, and its quality directly affects the accuracy of subsequent identification. At present, there are two common fingerprint collection technologies: optical fingerprint collection and capacitive fingerprint collection.
Optical fingerprinting: Using the principle of light reflection, put your finger on the collector, the light shines from the bottom to the surface of the fingerprint, and the reflected light is received by the optical sensor and forms a fingerprint image. This technique is less costly, but it is susceptible to factors such as stains, wetness and dryness.
Capacitive fingerprinting: Captures fingerprint information using the capacitance change between the finger and the sensor. When the finger touches the sensor, the distance between the various areas and the sensor is different due to the unevenness of the fingerprint, resulting in different capacitance values. These capacitance values are converted into a digital signal to form a fingerprint image. Capacitive fingerprint capture technology has high resolution and anti-interference ability, but the cost is relatively high.
Preprocessing is the process of optimizing the captured fingerprint image with the aim of improving image quality and recognition rate. Preprocessing mainly includes operations such as denoising, enhancement, and refinement.
Denoising: Removes background noise and interference information from fingerprint images to make fingerprint lines clearer.
Enhanced: The fingerprint image is enhanced by the algorithm to improve the contrast and clarity of the lines.
Refinement: The fingerprint lines are refined to the width of a single pixel, which is convenient for subsequent feature extraction and comparison.
Feature extraction is the process of extracting representative feature points from preprocessed fingerprint images. These feature points include the endpoints of the fingerprint's lines, bifurcations, etc., which constitute the feature template of the fingerprint. The quality of feature extraction algorithms directly affects the accuracy and speed of fingerprint recognition.
At present, commonly used feature extraction algorithms include algorithms based on detail points and algorithms based on grain structure. Each of these algorithms has its own advantages and disadvantages, and needs to be selected and optimized according to specific needs in practical applications.
Comparison and recognition is the process of comparing the extracted feature template with the template in the database. The matching algorithm is used to find out the template with the highest similarity, so as to realize the identification of individual identity. The quality of the comparison and recognition algorithms also affects the accuracy and speed of fingerprint recognition.
At present, the commonly used comparison and recognition algorithms include the matching algorithm based on detail points, the matching algorithm based on the grain structure, and the matching algorithm based on deep learning. These algorithms have different performance in different application scenarios, and need to be selected and optimized according to actual needs.
As an efficient and convenient biometric technology, fingerprint recognition technology has been widely used in various fields. However, with the continuous development of technology, fingerprint recognition technology also faces some challenges, such as security, privacy protection and other issues. In the future, with the continuous optimization of algorithms and the continuous emergence of new technologies, it is believed that fingerprint recognition technology will play a greater role in more fields and bring more convenience and security to our lives.
Fingerprints